As a default setting, most existing interpretation methods [1]}, [2]}, [3]}, [4]} are designed in the classification context where the output score is generated from one input image. Among the approaches that do not require architecture change, one intuitive idea [1]}, [3]} is to look into the model and see where contributes the most to the final prediction. CAM [1]} and its variant Grad-CAM [8]} are among the most widely used approaches, but recent works [4]}, [8]}, [11]} simply consider CAM as a heuristic linear combination of convolutional feature maps which is limited to the original architecture, global average pooling (GAP) and one FC layer [1]}, [8]}, [11]}. Specifically, to generate CAM for standard VGG [15]} with global max pooling (GMP) and three FC layers, [1]}, [11]} have to replace this architecture with GAP+FC and re-train the model.
In this paper, we show that CAM is actually a special case of activation decomposition on the specific architecture, and the activation decomposition idea applies to more complex architectures without re-training, even beyond the classification problem.
